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Synthesizing high-fidelity time-series sensor signals to facilitate machine-learning innovations

a high-fidelity, time-series sensor technology, applied in the direction of program control, instruments, testing/monitoring control systems, etc., can solve the problems of costly degradation of these components, routine deformation over time, failure,

Active Publication Date: 2022-07-19
ORACLE INT CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This patent describes a system that helps to develop machine-learning techniques to analyze data from a monitoring system. The system takes the original data and splits it into two parts: measurements that are consistent with the system's current state and measurements that are not. By combining these two sets of data, the system creates new data that cannot be distinguished from the original data. This new data is then used to teach a machine-learning system how to predict and prevent problems in the monitoring system.

Problems solved by technology

Electrical generation plants, such as gas-fired or coal-fired power plants, nuclear plants and wind farms, include numerous components, such as pumps, turbines and transformers, which routinely degrade over time and fail.
Degradation of these components can be costly.
Unfortunately, much of the time-series data associated with power generation, and transmission and distribution systems, which originates from utility companies and also smart meters in homes and businesses, has privacy contracts associated with it.

Method used

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  • Synthesizing high-fidelity time-series sensor signals to facilitate machine-learning innovations
  • Synthesizing high-fidelity time-series sensor signals to facilitate machine-learning innovations
  • Synthesizing high-fidelity time-series sensor signals to facilitate machine-learning innovations

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Embodiment Construction

[0023]The following description is presented to enable any person skilled in the art to make and use the present embodiments, and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present embodiments. Thus, the present embodiments are not limited to the embodiments shown, but are to be accorded the widest scope consistent with the principles and features disclosed herein.

[0024]The data structures and code described in this detailed description are typically stored on a computer-readable storage medium, which may be any device or medium that can store code and / or data for use by a computer system. The computer-readable storage medium includes, but is not limited to, volatile memory, non-volatile memory, magneti...

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Abstract

The disclosed embodiments relate to a system that facilitates development of machine-learning techniques to perform prognostic-surveillance operations on time-series data from a monitored system, such as a power plant and associated power-distribution system. During operation, the system receives original time-series signals comprising sequences of observations obtained from sensors in the monitored system. Next, the system decomposes the original time-series signals into deterministic and stochastic components. The system then uses the deterministic and stochastic components to produce synthetic time-series signals, which are statistically indistinguishable from the original time-series signals. Finally, the system enables a developer to use the synthetic time-series signals to develop machine-learning (ML) techniques to perform prognostic-surveillance operations on subsequently received time-series signals from the monitored system.

Description

BACKGROUNDField[0001]The disclosed embodiments generally relate to techniques for analyzing time-series sensor signals from a monitored system. More specifically, the disclosed embodiments relate to a technique for synthesizing high-fidelity time-series sensor signals to facilitate machine-learning innovations for various monitored systems, such as power plants and associated critical assets in electrical transmission and distribution grids.Related Art[0002]Electrical generation plants, such as gas-fired or coal-fired power plants, nuclear plants and wind farms, include numerous components, such as pumps, turbines and transformers, which routinely degrade over time and fail. Degradation of these components can be costly. To reduce such costs, it is advantageous to proactively monitor components in power plants and associated power-distribution grids to detect degradation early on, which makes it possible to fix impending problems while they are small. This type of proactive surveill...

Claims

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Application Information

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Patent Type & Authority Patents(United States)
IPC IPC(8): H04W4/38G06F17/18G06N20/00G05B19/048G06F17/14G06K9/62G06F16/2458G06K9/00
CPCG06N20/00G05B19/048G06F16/2474G06F17/14G06F17/18G06K9/0053G06K9/6255G06K9/6256H04W4/38G05B23/024G06F2218/10G06F18/28G06F18/214
Inventor GROSS, KENNY C.LI, MENGYINGWOOD, ALAN PAULJEFFREYS, STEVEN T.MISRA, AVISHKARFUMAGALLI, JR., LAWRENCE L.
Owner ORACLE INT CORP